{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda = e^(-10), w = [[ 0.01660577 -0.0477072   0.41365826  0.15553501  1.11561007 -1.28671007\n",
      "  -0.26342221  0.47832296]]\n",
      "lambda = e^(-9), w = [[ 0.01660576 -0.04770667  0.41365771  0.15553499  1.11560914 -1.28670963\n",
      "  -0.26342191  0.47832323]]\n",
      "lambda = e^(-8), w = [[ 0.01660572 -0.04770526  0.41365621  0.15553496  1.11560659 -1.28670843\n",
      "  -0.2634211   0.47832395]]\n",
      "lambda = e^(-7), w = [[ 0.01660563 -0.04770141  0.41365214  0.15553486  1.11559967 -1.28670516\n",
      "  -0.26341888  0.4783259 ]]\n",
      "lambda = e^(-6), w = [[ 0.01660536 -0.04769094  0.41364108  0.1555346   1.11558085 -1.28669629\n",
      "  -0.26341285  0.47833122]]\n",
      "lambda = e^(-5), w = [[ 0.01660465 -0.04766249  0.41361102  0.15553388  1.11552969 -1.28667218\n",
      "  -0.26339646  0.47834566]]\n",
      "lambda = e^(-4), w = [[ 0.0166027  -0.04758516  0.41352929  0.15553192  1.11539062 -1.28660663\n",
      "  -0.2633519   0.47838494]]\n",
      "lambda = e^(-3), w = [[ 0.01659742 -0.04737496  0.41330714  0.1555266   1.1150126  -1.28642845\n",
      "  -0.26323077  0.47849169]]\n",
      "lambda = e^(-2), w = [[ 0.01658305 -0.04680356  0.41270328  0.15551213  1.11398503 -1.2859441\n",
      "  -0.26290153  0.47878188]]\n",
      "lambda = e^(-1), w = [[ 0.01653884 -0.04549953  0.41113634  0.15545032  1.11529733 -1.28616464\n",
      "  -0.26296376  0.47804841]]\n",
      "lambda = e^(0), w = [[ 0.0164328  -0.04121551  0.4066233   0.15534385  1.10760417 -1.2825516\n",
      "  -0.2605118   0.48023131]]\n",
      "lambda = e^(1), w = [[ 0.01613973 -0.02951034  0.39416606  0.1550324   1.09055472 -1.27419467\n",
      "  -0.25476964  0.4847405 ]]\n",
      "lambda = e^(2), w = [[ 0.0153508   0.          0.36263561  0.15424449  1.03915517 -1.24943788\n",
      "  -0.23788783  0.49878292]]\n",
      "lambda = e^(3), w = [[ 0.01325127  0.          0.35059049  0.15288981  0.88507194 -1.17206133\n",
      "  -0.18460861  0.53975315]]\n",
      "lambda = e^(4), w = [[ 0.00765438  0.          0.32095796  0.14970101  0.37822709 -0.92846213\n",
      "  -0.01885195  0.68362244]]\n",
      "lambda = e^(5), w = [[ 0.          0.          0.26279139  0.14535091  0.         -0.60187314\n",
      "   0.          0.78431832]]\n",
      "lambda = e^(6), w = [[ 0.          0.          0.07663304  0.12601456  0.         -0.25525521\n",
      "   0.          0.63220029]]\n",
      "lambda = e^(7), w = [[ 0.          0.          0.          0.06282233  0.          0.          0.\n",
      "   0.44495654]]\n",
      "lambda = e^(8), w = [[ 0.          0.          0.          0.          0.          0.          0.\n",
      "   0.27074402]]\n",
      "lambda = e^(9), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(10), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(11), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(12), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(13), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(14), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(15), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(16), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(17), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(18), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "lambda = e^(19), w = [[ 0.  0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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L/zNXDbny5a7f+AmYQPthwN9mnjfgxRfo/h2q7GInMyGTrMQssj3ZZHmyyE7MvjLuySY/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105015e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run lasso_regression.py"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
